A real-life overview of a hematopoietic cell transplant program throughout a four-year period, including prospective registry, exclusion causes and final donor selection.

Traceability of patients who are candidates for Hematopoietic cell transplant (HCT) is crucial to ensure HCT program quality. Continuous knowledge of both a detailed registry from a HCT program and final exclusion causes can contribute to promoting a real-life vision and optimizing patient and donor selection. We analyzed epidemiological data reported in a 4 year-monocentric prospective registry, which included all patients presented as candidates for autologous (Auto) and/or allogeneic (Allo) HCT. A total of 543 patients were considered for HCT: 252 (42.4%) for Allo and 291 (57.6%) for Auto. A total of 98 (38.9%) patients were excluded from AlloHCT due to basal disease progression more commonly (18.2%). Seventy-six (30.2%) patients had an HLA identical sibling, whereas 147 (58.3%) patients had only Haplo. UD research was performed in 106 (42%) cases, significantly more often in myeloid than lymphoid malignancies (57% vs 28.7%, p < 0.001) but 61.3% were finally canceled, due to donor or disease causes in 72.4%. With respect to Auto candidates, a total of 60 (20.6%) patients were finally excluded; progression was the most common cause (12%). Currently, Haplo is the most frequent donor type. The high cancellation rate of UD research should be revised to optimize further donor algorithms.

Impact of the Inclusion of an Aminoglycoside to the Initial Empirical Antibiotic Therapy for Gram-Negative Bloodstream Infections in Hematological Neutropenic Patients: a Propensity-Matched Cohort Study (AMINOLACTAM Study).

To test the hypothesis that the addition of an aminoglycoside to a ß-lactam antibiotic could provide better outcomes than ß-lactam monotherapy for the initial empirical treatment of hematological neutropenic patients with subsequently documented Gram-negative bacillus (GNB) bloodstream infection (BSI), a multinational, retrospective, cohort study of GNB BSI episodes in hematological neutropenic patients in six centers (2010 to 2017) was conducted. Combination therapy (ß-lactam plus aminoglycoside) was compared to ß-lactam monotherapy. The primary endpoint was the case fatality rate, assessed at 7 and 30 days from BSI onset. Secondary endpoints were nephrotoxicity and persistent BSI. Propensity score (PS) matching was performed. Among 542 GNB BSI episodes, 304 (56%) were initially treated with combination therapy, with cefepime plus amikacin being most common (158/304 [52%]). Overall, Escherichia coli (273/304 [50.4%]) was the main etiological agent, followed by Pseudomonas aeruginosa, which predominated in the combination group (76/304 [25%] versus 28/238 [11.8%]; P < 0.001). Multidrug resistance rates were similar between groups (83/294 [28.2%] versus 63/233 [27%]; P = 0.95). In the multivariate analysis, combination therapy was associated with a lower 7-day case fatality rate (odds ratio [OR], 0.37; 95% CI, 0.14 to 0.91; P = 0.035) with a tendency toward lower mortality at 30 days (OR, 0.56; 95% CI, 0.29 to 1.08; P = 0.084). After PS matching, these differences remained for the 7-day case fatality rate (OR, 0.33; 95% CI, 0.13 to 0.82; P = 0.017). In addition, aminoglycoside use was not significantly associated with renal function impairment (OR, 1.12; 95% CI, 0.26 to 4.87; P = 0.9). The addition of an aminoglycoside to the initial empirical therapy regimen for febrile neutropenic hematological patients should be considered.

Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification.

In modern proteomics, prediction of protein-protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through the application of data mining techniques and provides very accurate models with high levels of sensitivity and specificity in the classification of PPIs. The well-known support vector machine paradigm is used to learn the models, which will also return a new confidence score which may help expert researchers to filter out and validate new external PPIs. One of the most-widely analyzed organisms, yeast, will be studied. We processed a very high-confidence dataset by extracting up to 26 specific features obtained from the chosen databases, half of them calculated using two new similarity measures proposed in this paper. Then, by applying a filter-wrapper algorithm for feature selection, we obtained a final set composed of the eight most relevant features for predicting PPIs, which was validated by a ROC analysis. The prediction capability of the support vector machine model using these eight features was tested through the evaluation of the predictions obtained in a set of external experimental, computational, and literature-collected datasets.

Generating balanced learning and test sets for function approximation problems.

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation.

This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.

Web newspaper layout optimization using simulated annealing.

The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the client's computer configuration, and supports articles with different widths.

A systematic approach to a self-generating fuzzy rule-table for function approximation.

In this paper, a systematic design is proposed to determine fuzzy system structure and learning its parameters, from a set of given training examples. In particular, two fundamental problems concerning fuzzy system modeling are addressed: 1) fuzzy rule parameter optimization and 2) the identification of system structure (i.e., the number of membership functions and fuzzy rules). A four-step approach to build a fuzzy system automatically is presented: Step 1 directly obtains the optimum fuzzy rules for a given membership function configuration. Step 2 optimizes the allocation of the membership functions and the conclusion of the rules, in order to achieve a better approximation. Step 3 determines a new and more suitable topology with the information derived from the approximation error distribution; it decides which variables should increase the number of membership functions. Finally, Step 4 determines which structure should be selected to approximate the function, from the possible configurations provided by the algorithm in the three previous steps. The results of applying this method to the problem of function approximation are presented and then compared with other methodologies proposed in the bibliography.